Kaitlyn HoodApplied Mathematics & Teaching

Teaching

Learning mathematics by doing mathematics.

I develop inclusive, hands-on learning experiences that connect mathematical ideas with computation, data, and applications.

Data Science Labs Study tips for students Calculus readiness skills Calc II: Calc I review Calc II readiness self-check

Instructional leadership

Data Science Labs

Since Fall 2023, I have coordinated the Data Science Labs sequence at Purdue University: hands-on laboratory courses that connect mathematics with modern applications in data science, engineering, and scientific computing. Through programming, sensor-based data collection, signal processing, image analysis, and computational modeling, students learn how abstract mathematical concepts are applied to real-world problems.

The labs emphasize active learning and interdisciplinary problem solving while giving students practical experience with hardware, coding, and real experimental data. The curriculum is fully open-source and supported through publicly available online textbooks developed and maintained at Purdue University.

Visit the Data Science Labs page

Signals & Systems for Mathematicians (MA 34900)

I developed MA 34900, “Signals & Systems for Mathematicians,” for the Applied Mathematics and Mathematical Data Science majors. The course integrates continuous and discrete Fourier analysis with applications in filtering, denoising, and image processing, while emphasizing the mathematical consequences of sampling and approximation.

The course includes programming-based homework assignments and collaborative in-class activities designed to strengthen both theoretical understanding and computational fluency. View the MA 34900 flier (PDF).

Large-lecture instruction and coordination

In large-lecture calculus courses, I have implemented evidence-based instructional practices including classroom response systems for formative assessment, collaborative recitation activities, and standardized quiz pools designed to improve consistency across recitation sections. I have also trained and mentored teaching assistants in active-learning practices for coordinated courses.

Open educational resources

The Data Science Labs are supported through free, open-source online textbooks developed at Purdue University and publicly disseminated through GitHub Pages. I continuously update these materials to improve mathematical exposition, preserve compatibility with evolving software tools, and incorporate new applications in data science, scientific computing, and signal processing.

Courses taught

Current and archived course materials remain on the established Purdue course pages.

Study tips & teaching resources

Study tips for mathematics students

For teaching assistants and course coordinators:

Training, development & recognition

In Spring 2026, I was a member of the Mathematics Community of Practice. In 2023, I was selected as a Teaching for Tomorrow Junior Fellow at Purdue.

I completed the Kaufman Teaching Certificate Program at MIT’s Teaching + Learning Lab in 2018, studying evidence-based teaching techniques, and completed IMPACT X Access at Purdue in 2020.

Teaching recognition: Honoree, Purdue Guru Vandana (Teacher Appreciation), Spring 2026, Purdue Hindu YUVA.